import pandas as pd
import numpy as np
import yfinance as yf
import random
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
import secrets
import plotly as plotly
import plotly.express as px
from tqdm import tqdm
import plotly.graph_objects as go
pd.set_option('plotting.backend', 'matplotlib')
plotly.offline.init_notebook_mode()
#warnings.simplefilter(action='ignore', category=FutureWarning)

TICKER="IWDA.AS"
NUMBER_OF_SIMULATIONS=1000
STARTING_CAPITAL = 10000
YEARS_OF_SIMULATION=30

YEARS_OF_SIMULATION=YEARS_OF_SIMULATION+1
Market_Days=253

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()

[*********************100%%**********************]  1 of 1 completed

df.plot()

<Axes: xlabel='Date'>

df

Date
2009-09-28    0.000000
2009-09-29    0.000000
2009-09-30    0.000000
2009-10-01    0.000000
2009-10-02    0.000000
                ...   
2023-11-09    0.004418
2023-11-10   -0.002393
2023-11-13    0.007003
2023-11-14    0.006568
2023-11-15    0.003582
Name: Adj Close, Length: 3622, dtype: float64

df_simulations=np.zeros((YEARS_OF_SIMULATION,NUMBER_OF_SIMULATIONS))
df_simulations[0,:]=STARTING_CAPITAL

for x in tqdm(range(0,NUMBER_OF_SIMULATIONS)):
    for i in range(1,YEARS_OF_SIMULATION):
        annual_change=1
        for k in range(Market_Days):
            annual_change=annual_change*(1+(secrets.choice(df.iloc[:])))
        df_simulations[i,x]=annual_change*df_simulations[i-1,x]
df_simulations= pd.DataFrame(df_simulations)
df_simulations_1=  pd.DataFrame(df_simulations)

100%|██████████| 1000/1000 [03:22<00:00,  4.95it/s]

df_simulations_1

df_simulations=df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION-1]

0      240377.999289
1      148272.715232
2      387555.297204
3      149026.207456
4      196248.731824
           ...      
995    333929.278063
996    339723.780659
997    231930.979538
998     81028.843693
999    217273.760962
Name: 30, Length: 1000, dtype: float64

quantile=df_simulations.iloc[YEARS_OF_SIMULATION-1].quantile([0.1,0.9])
quantile=list(quantile)
print(quantile)
df_simulations_purged=df_simulations[df_simulations.columns[ df_simulations.max() < quantile[1]]]
df_simulations_purged=df_simulations_purged[df_simulations_purged.columns[ df_simulations_purged.max() > quantile[0]]]

[77107.80451093665, 722493.1954776454]

df_simulations_purged.plot(figsize=(16,8), title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios", legend=False)

<Axes: title={'center': 'Simulation of 1000 portfolios'}>

df_simulations_purged.iloc[YEARS_OF_SIMULATION-1].plot.density(figsize=(16,8),fontsize=14, xlim=(-100000,900000))

<Axes: ylabel='Density'>

top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
df_statistics

#ax= df_statistics.plot(legend=None,logy=False,fontsize=20,figsize=(25,15),linewidth=4,color="black",title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios")
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

ls_simulations_at_profit=[]
for i in range(YEARS_OF_SIMULATION):
    c=len([1 for i in list(df_simulations.iloc[i])if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c/NUMBER_OF_SIMULATIONS*100)
pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit,columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon", labels= {"index": "Period length in years", "value":"Chance to be in profit"})

TICKER="^GSPC"

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()
df_simulations = np.zeros((YEARS_OF_SIMULATION, NUMBER_OF_SIMULATIONS))
df_simulations[0, :] = STARTING_CAPITAL

for x in tqdm(range(0, NUMBER_OF_SIMULATIONS)):
    for i in range(1, YEARS_OF_SIMULATION):
        annual_change = 1
        for k in range(Market_Days):
            annual_change = annual_change * (1 + (secrets.choice(df.iloc[:])))
        df_simulations[i, x] = annual_change * df_simulations[i - 1, x]
df_simulations = pd.DataFrame(df_simulations)
df_simulations_1 = pd.DataFrame(df_simulations)
df_simulations = df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION - 1]
quantile = df_simulations.iloc[YEARS_OF_SIMULATION - 1].quantile([0.1, 0.9])
quantile = list(quantile)
df_simulations_purged = df_simulations[df_simulations.columns[df_simulations.max() < quantile[1]]]
df_simulations_purged = df_simulations_purged[df_simulations_purged.columns[df_simulations_purged.max() > quantile[0]]]
top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

ls_simulations_at_profit = []
for i in range(YEARS_OF_SIMULATION):
    c = len([1 for i in list(df_simulations.iloc[i]) if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c / NUMBER_OF_SIMULATIONS * 100)

pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit, columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon",
                              labels={"index": "Period length in years", "value": "Chance to be in profit"})

!jupyter nbconvert --execute --to html Portfolio1.ipynb --HTMLExporter.theme=dark

	0	1	2	3	4	5	6	7	8	9	...	990	991	992	993	994	995	996	997	998	999
0	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	...	1.000000e+04	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000
1	12313.368079	10840.329743	12534.749650	7687.461219	9597.238724	10504.787151	11593.289800	13066.498185	10892.408553	9092.878159	...	1.289220e+04	8274.641896	8417.425507	14871.171902	16996.515440	9930.746417	10276.650672	10154.226516	12731.094972	11751.471399
2	13303.815309	9973.670746	19184.333676	8453.172699	11674.197753	14142.583734	10903.721436	10082.400191	12598.207368	8369.250287	...	1.402854e+04	12786.304914	9189.680820	17700.856270	16732.364996	8314.550980	11027.800910	11306.289640	10781.913329	11709.048027
3	16414.933792	10390.412961	17912.959775	8979.691504	16491.934235	15598.501539	11263.564228	11695.297533	11979.422208	8622.415527	...	1.462150e+04	16509.016624	11615.766194	20473.440202	23059.060898	10076.905410	11237.874621	14251.208558	10827.234434	12561.406480
4	17780.665601	14310.046252	24571.133799	9983.826905	16086.436354	14803.074173	12507.071491	13694.356004	10782.445705	10054.500035	...	1.615398e+04	12931.477983	16441.395710	19721.108779	23107.795807	12470.736007	12458.421972	16211.331076	10232.734681	15779.190060
5	19662.591981	17444.678303	30092.546212	13730.743680	20515.879817	12771.902110	13084.471496	14577.679356	11938.104495	9319.563972	...	1.769999e+04	13379.504387	24783.277005	17180.261234	31159.245051	14272.928375	14241.566165	20165.330111	11537.095960	17710.357345
6	24724.293231	17908.797461	28109.207649	17586.382679	20756.437085	14994.499497	12826.700016	16229.888510	13186.232550	7583.554704	...	1.704240e+04	16436.862368	25128.482984	20497.506966	41526.855050	14532.203469	16419.685271	23012.029959	12734.301750	15811.116475
7	26870.685969	19181.851998	27138.246144	18055.699638	21016.434914	15457.620936	12496.136742	19342.220823	15374.527672	10198.466927	...	2.213815e+04	18580.759614	23392.800767	23496.664367	48830.313101	19822.901292	15764.151719	29068.061535	12379.445756	19618.532713
8	28364.929989	19690.376303	27133.262451	14380.166818	23056.063207	16661.940125	14216.047540	16806.205708	23697.830062	10208.561808	...	3.129905e+04	20624.090058	33047.079222	26216.930707	54948.511260	24056.504079	17085.186959	32982.928774	12609.287810	22335.700548
9	21435.751203	16659.597981	32916.813876	16688.883268	21485.893992	16006.937047	14455.631334	16813.512634	30175.329717	11853.012990	...	3.696291e+04	18802.404569	37703.535015	37438.871154	49275.898620	26768.662262	22911.900618	41017.897443	15625.323866	20771.394192
10	34411.930605	16450.835483	30857.292342	14447.151756	21217.345715	17263.889039	14143.594347	19241.401966	32836.033114	10491.262187	...	3.896243e+04	22886.052293	41208.553923	38412.908630	45504.610773	34727.437382	25670.124114	44651.775568	18201.521963	24385.687233
11	37313.637894	21527.637729	35408.454845	15464.789264	21715.206197	19416.984384	14537.248687	21126.984870	40936.545960	9524.201521	...	3.512628e+04	22075.853908	50681.666565	37752.203184	57493.000542	38549.358061	28039.234019	44892.490229	17393.278431	22273.664565
12	42219.465799	25908.992702	36422.526854	17880.059039	28797.927656	21601.946891	17042.298566	21865.466641	44774.183203	11393.314445	...	4.455291e+04	26300.790348	64247.582424	46716.700976	71211.540819	58003.673568	39808.703149	42597.367462	19169.354906	26711.926616
13	40858.421164	28704.555726	42682.721745	20705.902232	34945.887249	19429.798128	19341.362992	18728.234119	51487.851186	10598.535387	...	5.386171e+04	20339.207601	53006.029123	51703.407535	72586.762203	72804.971313	40764.571118	56625.203349	18658.082373	34483.019150
14	50576.480520	31914.794552	37610.259066	21203.736565	31152.538629	18547.781703	26096.766561	16469.909818	58385.088184	10213.265114	...	7.581662e+04	22142.817684	57498.582225	61409.822498	76578.545128	76958.259941	46152.925105	70877.368581	20716.208069	59040.599507
15	56203.350367	43029.089262	42801.965884	20683.166026	30644.781218	15591.238827	28187.001203	18068.497556	75980.453048	9294.673834	...	1.042675e+05	22412.130199	69491.259376	77931.388960	94800.367175	73916.061234	57833.151844	68232.394318	22449.562581	66263.743268
16	65531.569348	52508.536325	45257.399508	23589.335981	38519.809718	16945.671066	30403.258969	17607.134542	96747.092610	9919.680061	...	1.197204e+05	25672.673422	103981.683561	74766.453352	113748.191541	76967.430808	69490.935923	57258.591070	21141.673865	83681.353593
17	75049.399598	56588.459175	59151.890101	34529.586253	38542.264059	20220.536155	38972.746751	24141.431159	114336.590366	11862.871690	...	1.236718e+05	29938.830894	109935.694435	57112.932014	112566.882129	99855.042464	74887.821055	62559.185461	20448.622061	75342.646491
18	81978.734598	56705.308780	90867.736407	40983.236107	43359.406779	16217.552392	45131.294845	33645.889985	105963.440540	16996.334739	...	1.642433e+05	27695.956254	136110.897887	88494.065995	135855.938620	115044.901980	69742.897636	55518.476505	23309.735032	138700.064428
19	82255.767710	66608.156543	124491.047097	47975.459602	56663.938339	21488.865096	62685.410862	25291.202925	143584.589374	23181.097481	...	2.140890e+05	30899.022740	142556.973950	96742.401484	132638.009605	145818.668159	85225.084386	61029.042422	23007.973352	133364.855960
20	92519.845454	67097.730683	132857.326297	49545.404534	56009.903132	20156.819741	67734.933928	30851.729547	135498.695531	25185.864241	...	2.927718e+05	32003.971454	149092.447996	121492.838085	170416.146224	167123.490917	82999.512523	61769.441488	29207.377857	131914.316863
21	93126.898828	73488.792731	195379.448617	42686.611621	58210.451453	24485.263774	87226.308111	33504.672679	141869.511028	29932.059963	...	3.152220e+05	34611.075387	176099.609374	134068.211586	175911.328516	110157.644733	85824.447658	72203.588757	31933.824676	150771.851528
22	98907.810613	77318.368912	223121.879654	44096.557571	72864.947233	34267.919014	120865.308457	31310.746524	196512.049043	40261.136491	...	3.939012e+05	46073.709567	190730.045606	107375.997010	217101.173174	143523.069373	97838.342271	89518.838485	32778.273963	152562.830035
23	166054.723679	68917.463398	234515.441820	41941.582464	90243.735799	40236.939963	105927.257583	42561.001581	219434.265544	62753.252379	...	4.291681e+05	49269.780402	208612.368599	124083.828757	340431.102849	122364.921536	101729.228230	104827.992872	38673.202973	138057.683691
24	144805.011210	74483.652259	232135.489495	49532.377791	124089.864518	56322.966374	119116.195987	48374.828764	247028.119556	72810.576403	...	5.280528e+05	43007.446413	220108.777063	148980.867555	464935.376559	149845.664658	122912.086124	99073.373247	48693.817105	163157.808913
25	145392.040670	67086.595306	233757.035881	72490.110921	124225.553373	49297.395422	168394.348460	52859.716886	327855.849657	97578.315184	...	6.115322e+05	50169.940769	294816.461932	175022.371948	492621.920895	141043.344428	137474.765379	139160.077891	46683.736938	175515.479592
26	162648.091757	82285.190738	297468.924164	83074.767381	141498.043553	55192.441419	171771.193422	47470.441524	447606.316005	119206.636148	...	7.880187e+05	48142.369457	312902.011847	180889.133799	499579.607118	176206.983013	172790.249051	162256.079474	43633.208895	206603.726424
27	192162.418056	89874.832247	410338.124143	96328.778071	125238.563268	58704.866866	193800.927425	45849.314560	455922.898430	169100.843006	...	1.356221e+06	50005.618154	324727.437642	229473.043969	537756.886293	228506.772257	203117.710784	167031.201320	51457.384665	268911.611947
28	217320.414180	117625.464232	418249.220724	134878.370759	106296.719793	76693.923606	166291.276848	53841.226188	539213.562380	185588.964710	...	1.602376e+06	62970.168268	436585.872715	256943.889911	564933.227020	237210.389591	268932.742116	156718.530453	60747.727654	286710.898382
29	233174.039080	124143.641503	401251.381888	125575.836243	130271.735099	108155.197672	260891.134472	74353.757893	738108.751432	186077.187657	...	1.460656e+06	62411.318361	413874.778940	301208.752440	665886.087030	265368.157304	275959.410861	211209.668636	83762.352291	255689.601318
30	240377.999289	148272.715232	387555.297204	149026.207456	196248.731824	111558.226816	369463.166833	70370.818321	651561.547213	187923.809722	...	1.751063e+06	77893.329144	394671.158549	330067.415847	862084.427369	333929.278063	339723.780659	231930.979538	81028.843693	217273.760962

	top 25%	median	bottom 25%
0	10000.000000	10000.000000	10000.000000
1	12348.333554	11109.041030	10006.396264
2	14200.429839	12171.658750	10644.066110
3	16418.635680	13676.767940	11431.689400
4	18909.276997	15447.853332	12445.011851
5	21832.280496	17103.455678	13383.796337
6	24311.712250	19291.270609	15083.357567
7	27630.160441	21455.566453	16404.243026
8	31317.266818	23742.298014	17702.520834
9	35751.516445	26709.509849	19684.756245
10	40584.526039	29610.078725	21076.053059
11	46602.194852	32579.591949	23238.045354
12	52432.647163	36351.367744	24959.929030
13	58051.044836	40300.344959	27653.064242
14	64439.277873	44676.567088	29033.616924
15	73011.253750	49596.605492	32436.192026
16	81804.045282	54934.059045	35382.782965
17	91913.439542	60081.754947	38706.836915
18	104947.531211	68010.698888	41766.153084
19	119253.678346	76220.717255	44500.758223
20	133079.264218	85132.677985	51414.917013
21	149362.353497	93444.763122	54921.073782
22	172351.892497	105134.006520	59123.171448
23	190731.978616	116989.207605	67873.036707
24	214912.293196	128330.086687	73917.881840
25	241144.827277	143528.700869	83644.505990
26	272189.611973	160624.673892	89960.393452
27	300701.248346	177930.928963	100589.507553
28	340332.156706	192349.197636	107897.510349
29	387153.937670	217552.771247	122377.502987
30	429483.031623	240061.472868	134759.454432

PORTFOLIO ONLY IWDA